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Found 2 papers, 1 papers with code
On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions
In this work, we assess attribution methods from a perspective not previously explored in the graph domain: retraining.
LAP: An Attention-Based Module for Concept Based Self-Interpretation and Knowledge Injection in Convolutional Neural Networks
Despite the state-of-the-art performance of deep convolutional neural networks, they are susceptible to bias and malfunction in unseen situations.
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